What does high bias typically indicate about a model's predictions?

High bias in a model typically indicates that the model makes predictions that are consistent but fail to capture the underlying patterns in the data. This situation generally arises when a model is too simplistic or not complex enough to learn the relationships between the input features and the target outcomes. As a result, the model may produce similar predictions across different datasets, showing low variability. However, the downside is that it does not adequately adapt to the nuances of the data, leading to systematic errors. This phenomenon can result in poor performance on both training and unseen validation or test datasets, demonstrating the model’s inability to generalize effectively.